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  What is Pandas?  pandas provide rich data structures and functions designed to make working with structured data fast, easy, and expressive. It is, as you will see, one of the critical ingredients enabling Python to be a powerful and productive data analysis environment. The primary object in pandas that will be used in this book is the DataFrame, a twodimensional tabular, column-oriented data structure with both row and column labels: >>>  frame total_bill tip sex smoker day time size  1 16.99 1.01 Female No Sun Dinner 2  2 10.34 1.66 Male No Sun Dinner 3  3 21.01 3.5 Male No Sun Dinner 3  4 23.68 3.31 Male No Sun Dinner 2  5 24.59 3.61 Female No Sun Dinner 4  6 25.29 4.71 Male No Sun Dinner 4  7 8.77 2 Male No Sun Dinner 2  8 26.88 3.12 Male No Sun Dinner 4  9 15.04 1.96 Male No Sun Dinner 2  10 14.78 3.23 Male No Sun Dinner 2  pandas combine the high-performance array-computing features of NumPy with t...

What is NumPy ??  NumPy, short for Numerical Python, is the foundational package for scientific computing in Python. The majority of this book will be based on NumPy and libraries built on top of NumPy. It provides, among other things:  • A fast and efficient multidimensional array object ndarray  • Functions for performing element-wise computations with arrays or mathematical operations between arrays  • Tools for reading and writing array-based data sets to disk  • Linear algebra operations, Fourier transform, and random number generation  • Tools for integrating connecting C, C++, and Fortran code to Python  Beyond the fast array-processing capabilities that NumPy adds to Python, one of its primary purposes with regards to data analysis is as the primary container for data to be passed between algorithms. For numerical data, NumPy arrays are a much more efficient way of storing and manipulating data than the other built-in Python data structures. Al...

Why Not Python? While Python is an excellent environment for building computationally-intensive scientific applications and building most kinds of general-purpose systems, there are a number of uses for which Python may be less suitable. As Python is an interpreted programming language, in general, most Python code will run substantially slower than code written in a compiled language like Java or C++. As programmer time is typically more valuable than CPU time, many are happy to make this tradeoff. However, in an application with very low latency requirements (for example, a high-frequency trading system), the time spent programming in a lower-level, lower-productivity language like C++ to achieve the maximum possible performance might be time well spent. Python is not an ideal language for highly concurrent, multithreaded applications, particularly applications with many CPU-bound threads. The reason for this is that it has what is known as the global interpreter lock (GIL), a mechan...

  Why Python for Data Analysis?  For many people (myself among them), the Python language is easy to fall in love with. Since its first appearance in 1991, Python has become one of the most popular dynamic, programming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular in recent years for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages as they can be used to write quick-and-dirty small programs or scripts. I don’t like the term “scripting language” as it carries a connotation that it cannot be used for building mission-critical software. Among interpreted languages, Python is distinguished by its large and active scientific computing community. Adopttion of Python for scientific computing in both industrial applications and academic research has increased significantly since the early 2000s.   For data analysis and interactive, explo...

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  How Can You Transfer your career to Data Analytics? No matter what field you come from and what work experience you have, you can steer your career into Data Analytics . If you are still wondering what your next steps should be, here is a comprehensive guide that you can refer to. Based on your background, what skills you need to pick up to transition to a data-driven profession, we list down what you can expect! Are you a Fresher? The good news is that a lot of companies tend to hire fresh college graduates and train them in-house. They need an unbiased, fresh pair of eyes to look at their business problems. As a fresher, you don’t have any baggage, and the biggest advantage is that, as a professional, you can be molded in any way.  Make yourself conceptually sound in statistics, learn relevant tools and languages to demonstrate your skillset.   If you have technology experience , for instance in software engineering, or if you are an IT professional, then you certa...

  Machine Learning with Python: Zero to GBMs Machine Learning with Python: Zero to GBMs A beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python and its ecosystem of ML libraries: scikit-learn, XGBoost, and LightGBM. Earn a verified certificate of accomplishment by completing practical assignments and building a real-world course project. Watch hands-on coding-focused video tutorials Practice coding with cloud Jupyter notebooks Build an end-to-end real-world course project Earn a verified certificate of accomplishment Interact with a global community of learners You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world dataset. Prerequisite:  Data Analysis with Python: Zero to Pandas . Lesson 1 - Linear Regression with Scikit Learn Preparing data for machine learning Linear regression with multiple features Generating predictions and evaluating models Lesson ...